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Measuring the distances in the location-aware keyword query suggestion is an important problem. The distance function will be crucial parameter in deciding the précised results. The decision of the results which we obtain will create the issue for the obtained outcome. In the following, Table 2.3 listed the different measures which are used in measuring the documents. Measuring the distance for spatial data and the keyword data are important in this case. The relevance of the retrieved results is very crucial in measuring the performance of the techniques. So, the relevance of the measures is so important in the document. We analyzed the different papers which are used for the different proximity measures for analyzing the relevance.

As shown in Table 2.3, many techniques will provide the user to retrieve the query; based on the different approaches, these may be good for some queries, and it may depend on the retrieved document based on the retrieval of the query [23]. The query suggestion is so important in these days; based on the location, their location, their habits, and interests may change like food preferences, usage items in their locations, it will be purely depends on the perception of the user’s location.

Table 2.3 Different approaches for the query suggestion techniques.

S. no	Techniques
1	Index
2	Rank
3	Popularity
4	No of times referred
5	Index + location
6	Document proximity [4]
7	AI-based search
8	Keyword-document graph

The different measures, which are used in the query suggestion techniques, are listed here.

1 Euclidean

2 Manhattan

3 Cosine similarity

4 Jaccard coefficients

The AI perspective will consider the following measures in query preparation, to enhance the performance of the query suggestion.

1 User’s information

2 Location

3 Previous search history

4 Back links

5 Keywords

6 Click through rate

7 Choice of websites

8 Other similar users’ choice

The AI algorithm learns from the results and decides the importance to be given to each of the factors specific to the user location. An AI-powered search engine learns and adjusts itself based on the ambiguous search queries; and it uses feedback data to improve the accuracy of its results.